AI Security: Securing LLMs, ML, Agents & Infra

At a Glance

AI Security is a professional DATA & AI eBook by Dargslan, 536 pages, available as an instant PDF and EPUB download for €26.90 with lifetime access and free updates. Securing Large Language Models, Machine Learning Systems, AI Agents, and Enterprise AI Infrastructure.

Length: 536 pages
Format: PDF and EPUB (instant download)
Language: English
Topic: DATA & AI
Edition: 1st Edition
Price: €26.90

Key Highlights

Comprehensive, practitioner-focused defense of enterprise AI systems
A security-first mental model for every layer of the AI stack
Security across the full AI lifecycle, from dataset to production
Dataset security, training pipeline hardening, and model integrity
Deep coverage of LLM security: prompt injection, jailbreaks, and context window exploitation
Hallucinations, trust failures, and how to defend against them
AI agent security and agent authorization boundaries
RAG architecture security and embedding security
AI infrastructure, container, API, and secrets management hardening
Privacy and data leakage prevention
AI monitoring, incident response, governance, standards, and frameworks
Building secure AI platforms and applying AI red teaming
Extensive appendices: security, prompt injection, RAG, agent, and deployment checklists; risk assessment template; incident response playbook; tool catalog; and glossary

Overview

A comprehensive, practitioner's guide to defending enterprise AI—LLMs, ML systems, and autonomous agents. Covers prompt injection, jailbreaks, training pipeline and dataset security, RAG and embedding defense, agent authorization, infrastructure hardening, governance, and AI red teaming.

The Problem

AI has become critical enterprise infrastructure—running customer service, financial decisions, healthcare diagnostics, and autonomous agents that act on the business's behalf. But it arrived faster than security did. In the race to deploy LLMs, ML pipelines, and agents, security has too often been bolted on as an afterthought, if it was considered at all.

The deeper problem is that traditional cybersecurity frameworks simply weren't built for these systems. Nothing in classic security models accounts for software that generates its own outputs, reasons over untrusted context, or acts autonomously across your environment. AI opens entirely new attack surfaces—prompt injection, jailbreaking, training data poisoning, model extraction, embedding manipulation, agent authorization abuse—that didn't exist a decade ago. Defending against them with yesterday's playbook leaves high-stakes, decision-making systems dangerously exposed.

The Solution

AI Security gives practitioners the new security thinking these systems demand. It's a book about AI risk and the disciplined engineering required to manage it—delivering actionable frameworks, not just theory, for defending LLMs, ML systems, autonomous agents, and enterprise AI infrastructure.

You'll build a security-first mental model for every layer of the stack, working across the full AI lifecycle: dataset integrity and training pipeline hardening; LLM vulnerabilities like prompt injection, jailbreaks, and context window exploitation; RAG, embedding, and agent authorization security; and infrastructure, container, API, and secrets hardening in production. Governance, monitoring, incident response, and red teaming round out a complete program. Backed by checklists, a risk assessment template, an incident response playbook, and a tool catalog, this book helps you build AI systems that are not just powerful, but genuinely secure.

About This Book

AI Security: Securing Large Language Models, Machine Learning Systems, AI Agents, and Enterprise AI Infrastructure is a comprehensive, practitioner-focused guide to defending the systems that increasingly define how businesses operate and compete. AI now powers customer service, financial decisions, healthcare diagnostics, and autonomous agents that act on our behalf—yet in the race to deploy, security has too often been an afterthought rather than a foundation. This book exists to close that gap.

This is not a book about AI capabilities—it's a book about AI risk, and the disciplined engineering practices required to manage it. If you're responsible for building, deploying, or defending AI in production, it gives you actionable frameworks rather than theory.

Security Built for a New Kind of System

Traditional cybersecurity frameworks were never designed for systems that generate their own outputs, reason over untrusted context, or act autonomously across enterprise environments. AI introduces novel attack surfaces: prompt injection, jailbreaking, training data poisoning, model extraction, embedding manipulation, and agent authorization abuse—threats that didn't exist a decade ago and that demand new security thinking. This book addresses that reality head-on.

The Full AI Lifecycle

You'll examine security across the entire AI lifecycle: from dataset integrity and training pipeline hardening, through LLM-specific vulnerabilities like prompt injection and context window exploitation, to the operational challenges of securing agents, APIs, containers, and secrets in production. The book also addresses the governance layer—privacy, compliance, incident response, and the emerging standards that will shape how AI security is regulated and audited.

What You'll Gain

Readers develop a security-first mental model for every layer of the AI stack. You'll learn to:

Assess and mitigate threats unique to LLMs, ML systems, and autonomous agents
Harden training pipelines and datasets against poisoning and integrity attacks
Defend against prompt injection, jailbreaks, and hallucination-driven trust failures
Secure RAG architectures, embeddings, and agent authorization boundaries
Build resilient AI infrastructure, from container security to secrets management
Establish governance, monitoring, and incident response programs tailored to AI systems
Apply red teaming methodologies to proactively test your AI security posture

Deep Technical Coverage Where It Counts

The book progresses deliberately: foundational concepts and threat landscapes first, followed by deep technical treatment of ML and LLM security, then agent and infrastructure security, and finally governance, standards, and future-facing challenges. Along the way it tackles the topics defining modern AI security—prompt injection and jailbreak techniques, hallucinations and trust, context window security, AI agent security and authorization, RAG security, embedding security, and model integrity—giving each the rigorous attention it deserves.

From Threats to Secure Platforms

Understanding threats is only half the discipline; this book carries you through to building resilient systems. You'll secure AI infrastructure, containers, APIs, and secrets; establish privacy and data leakage prevention; stand up AI monitoring and incident response; and align with governance, standards, and frameworks. The later chapters bring it all together—building secure AI platforms, applying AI red teaming, and delivering secure AI projects—while looking ahead to the future of AI security.

Reference Material for Immediate Use

Extensive appendices are designed for immediate application in your own organization: an AI security checklist, a prompt injection checklist, a secure RAG checklist, a secure agent checklist, an LLM deployment checklist, an AI risk assessment template, an AI incident response playbook, an AI security tool catalog, and a glossary.

Who This Book Is For

Whether you're a security engineer, ML practitioner, platform architect, or CISO, this book equips you with actionable frameworks for securing AI systems that are already shaping critical decisions. The tone throughout is practitioner-focused and unflinching—rigorous where rigor is warranted, but always oriented toward actionable defense. For every reader who intends to build AI systems that are not just powerful, but secure, this work is for you.

Who Is This Book For?

Security engineers and architects defending enterprise AI systems
ML practitioners and AI engineers who need to secure what they build
Platform and infrastructure architects deploying AI in production
CISOs and security leaders responsible for AI risk and governance
AI red teamers and offensive security professionals testing AI systems
DevSecOps engineers securing AI pipelines, agents, and APIs
Teams building LLM applications, RAG systems, or autonomous agents at scale

Who Is This Book NOT For?

Readers seeking a general machine learning or model-building tutorial rather than security
Those wanting only a high-level, non-technical AI ethics discussion
Complete beginners with no technical or security background
Hobbyists looking for casual, small-scale AI tips rather than enterprise-grade defense
Anyone expecting pure theory without checklists, frameworks, or actionable controls

Introduction to AI Security
AI Threat Landscape
AI System Architecture
ML Security Fundamentals
Dataset Security
Training Pipeline Security
Model Integrity
LLM Architecture Security
Prompt Injection Attacks
Jailbreak Techniques
Hallucinations & Trust
Context Window Security
AI Agent Security
Agent Authorization
RAG Security
Embedding Security
AI Infrastructure
Container Security
API Security
Secrets Management
Privacy
Data Leakage Prevention
AI Monitoring
AI Incident Response
AI Governance
Standards & Frameworks
Building Secure AI Platforms
AI Red Teaming
Secure AI Projects
Future of AI Security
Appendix: AI Security Checklist
Appendix: Prompt Injection Checklist
Appendix: Secure RAG Checklist
Appendix: Secure Agent Checklist
Appendix: LLM Deployment Checklist
Appendix: AI Risk Assessment Template
Appendix: AI Incident Response Playbook
Appendix: AI Security Tool Catalog
Appendix: AI Security Glossary

Requirements

A technical background in security, software engineering, or machine learning
Familiarity with core cybersecurity concepts (threats, controls, defense in depth)
Understanding of how LLMs, ML systems, or AI agents work is strongly helpful
Comfort with APIs, containers, and cloud infrastructure for the hands-on chapters
Access to a test environment to apply the frameworks, checklists, and projects
This is a practitioner-level book; enterprise or production AI context is assumed

Frequently Asked Questions

Q: How is this different from a general "AI security fundamentals" book?

A: This is a comprehensive, practitioner-level and enterprise-focused treatment. It goes deep on modern topics like AI agent security, agent authorization, RAG security, embedding security, model integrity, and AI red teaming, across the full lifecycle from dataset to production platform.

Q: Do I need a machine learning background?

A: A solid technical background helps, and understanding how LLMs, ML systems, or agents work is strongly beneficial. The book focuses on defending these systems rather than teaching you to build models from scratch.

Q: Does it cover AI agents and autonomous systems?

A: Yes, extensively. Dedicated chapters cover AI agent security and agent authorization—including the novel risk of agents that act autonomously across enterprise environments—plus a secure agent checklist in the appendices.

Q: Does it cover RAG and embeddings?

A: Yes. There are dedicated chapters on RAG security and embedding security, plus a secure RAG checklist, reflecting how central retrieval-augmented systems have become to enterprise AI.

Q: Is prompt injection covered in depth?

A: Yes. Dedicated chapters address prompt injection attacks, jailbreak techniques, and context window security, and the appendices include a prompt injection checklist for practical application.

Q: Does the book address governance and compliance, not just technical controls?

A: Yes. It covers privacy, data leakage prevention, AI governance, and standards and frameworks, recognizing that sustainable AI security requires policy, oversight, and regulatory alignment alongside technical defenses.

Q: What is AI red teaming, and is it included?

A: AI red teaming is proactively testing your AI systems the way an adversary would, to find weaknesses before attackers do. A dedicated chapter covers red teaming methodologies you can apply to your own systems.

Q: Are there ready-to-use resources for my organization?

A: Yes. The appendices are built for immediate use, including multiple checklists, an AI risk assessment template, an incident response playbook, a security tool catalog, and a glossary.